numpy:零均值数据和标准化 [英] Numpy:zero mean data and standardization

问题描述

我在教程中看到(没有进一步的解释)，我们可以使用x -= np.mean(x, axis=0)将数据处理为零均值，并使用x /= np.std(x, axis=0)将数据归一化.谁能详细说明这两段代码，我从文档中得到的唯一信息就是np.mean计算算术平均值计算沿特定轴的平均值，而np.std这样做是针对标准偏差的.

I saw in tutorial (there were no further explanation) that we can process data to zero mean with x -= np.mean(x, axis=0) and normalize data with x /= np.std(x, axis=0). Can anyone elaborate on these two pieces on code, only thing I got from documentations is that np.mean calculates arithmetic mean calculates mean along specific axis and np.std does so for standard deviation.

推荐答案

这也称为 SciPy具有实用程序:

SciPy has a utility for it:

    >>> from scipy import stats
    >>> stats.zscore([ 0.7972,  0.0767,  0.4383,  0.7866,  0.8091,
    ...                0.1954,  0.6307,  0.6599,  0.1065,  0.0508])
    array([ 1.1273, -1.247 , -0.0552,  1.0923,  1.1664, -0.8559,  0.5786,
            0.6748, -1.1488, -1.3324])

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